ArticleScientific reports2020
Label-free detection of rare circulating tumor cells by image analysis and machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- Representation learning enables robust single cell phenotyping in whole slide liquid biopsy imaging.Scientific reports · 2025Article
- Versatile Image-Assisted Cell Sorting by Selective Trapping with Spatiotemporal Multiparameter Targeting.ACS sensors · 2025Article
- A dual-branch deep learning network for circulating tumor cells classification.Journal of translational medicine · 2025Article
- Circulating tumor cells: Blood-based detection, molecular biology, and clinical applications.Cancer cell · 2025Review
- Article
- Contrastive Representation Learning for Single Cell Phenotyping in Whole Slide Imaging of Enrichment-free Liquid Biopsy.bioRxiv : the preprint server for biology · 2025Article
- An integrated approach of feature selection and machine learning for early detection of breast cancer.Scientific reports · 2025Article
- Review
- MIML: multiplex image machine learning for high precision cell classification via mechanical traits within microfluidic systems.Microsystems & nanoengineering · 2025Article
- Article
- Deep learning-based lung cancer risk assessment using chest computed tomography images without pulmonary nodules ≥8 mm.Translational lung cancer research · 2025Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- Review
- Acoustofluidic Actuation of Living Cells.Micromachines · 2024Review
- Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.Stem cell research & therapy · 2024Article
- Dissemination of Circulating Tumor Cells in Breast and Prostate Cancer: Implications for Early Detection.Endocrinology · 2024Review
- Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.Research square · 2023Article
- An introduction to representation learning for single-cell data analysis.Cell reports methods · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Detection and characterization of rare circulating tumor cells (CTCs) in patients' blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of CTCs from patient blood samples, by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network, a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and CTCs. It requires minimal data pre-processing and has an easy experimental setup. Through our experiments, we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying CTCs. With more data becoming available in the future, the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for CTC analysis.
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